Here we show the code to reproduce the analyses of: Risso and Pagnotta (2019). Within-sample standardization and asymmetric winsorization lead to accurate classification of RNA-seq expression profiles. In preparation.
This file belongs to the repository: https://github.com/drisso/awst_analysis.
The code is released with license GPL v3.0.
Install and load awst
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("drisso/awst")
library(readr)
library(awst)
library(EDASeq)
library(Rtsne)
library(umap)
library(dendextend)
Data import and cleaning
The data are available on GEO with the following accession number.
In the following chunks of code, we arrange a data-frame where each of the cell are annotated, and the annotations are coded as colors.
We mostly follow the original article for data preprocessing.
Note that here we download and read the data directly from the remote gzipped files available in GEO. In some cases, with unstable internet connections, the following chunks may not work. In such cases, we suggest to “manually” downaload the data using the links provided above and read the data in R from a local copy of the file.
ADT and annotation
if(!file.exists("annotation.RData")) {
positive.col <- "red"
negative.col <- "gray"
ttable <- read_csv("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE100866&format=file&file=GSE100866_PBMC_vs_flow_10X-ADT_umi.csv.gz")
ttable <- as.data.frame(ttable)
rownames(ttable) <- ttable[,1]
ttable <- ttable[,-1]
ttable <- 1 + t(ttable)
tmp <- exp(log(apply(ttable, 1, prod))/ncol(ttable))
ttable <- log(ttable/tmp)
ttable <- 6 * (pnorm(ttable) - 0.5)
annotation.df <- as.data.frame(ttable)
annotation.df$cell <- rownames(annotation.df)
nBins <- 9
bbreaks <- seq(-3, 3, length.out = nBins + 1)
levels_cols <- c("green4", "green3", "green2", "green", "gray75", "red", "red2", "red3", "red4")
j <- 0
while(j < ncol(ttable)) {
j <- j + 1
annotation.df$tmp <- cut(ttable[, j], breaks = bbreaks, include.lowest = TRUE)
levels(annotation.df$tmp) <- levels_cols
colnames(annotation.df)[ncol(annotation.df)] <- paste0(colnames(ttable)[j], ".col")
}
save(annotation.df, file = "annotation.RData")
} else {
load("annotation.RData")
}
Single-cell RNA-seq
Before applying normalization and AWST, we filtered out cells that did not met the following criteria:
remove the genes detected in no cells.
filter out cells not having at least 500 observed features.
filter out cells not having at least 20 different intensities across the features.
if(!file.exists("Level3.RData")) {
ddata <- read_csv("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE100866&format=file&file=GSE100866_PBMC_vs_flow_10X-RNA_umi.csv.gz")
#restrict the features to the HUMAN ones
ddata <- ddata[grep("^HUMAN_", ddata$X1),]
ddata <- as.data.frame(ddata)
rownames(ddata) <- gsub("HUMAN_", "", ddata$X1)
ddata <- as.matrix(ddata[,-1])
save(ddata, file = "Level3.RData")
} else {
load("Level3.RData")
}
Apply AWST
if(!file.exists("expression.RData")) {
ddata <- t(ddata)
###
no_of_detected_gene_per_sample <- rowSums(ddata > 0)
fivenum(no_of_detected_gene_per_sample)# 10 638 739 873 4833
# restrict the collection of cells to those cells having at least 500 observed features
sum(no_of_detected_gene_per_sample > 500) # 7613
dim(ddata <- ddata[no_of_detected_gene_per_sample > 500,])#[1] 7613 17014
###
no_of_different_intensities <- apply(ddata, 1, function(x) length(table(x)))
fivenum(no_of_different_intensities)# 9 17 20 24 105
# restrict the collection of cells to those cells having at least 20 different intensities across the featutures
dim(ddata <- ddata[which(no_of_different_intensities >= 20), ])#[1] 4123 17014
###
# apply the full quantile normalization
normCounts <- EDASeq::betweenLaneNormalization(t(ddata), which = "full", round = FALSE)
save(normCounts, file = "normCounts.RData")
###
# apply the AWS-transformation
library(awst)
dim(exprData <- awst(normCounts, poscount = TRUE, full_quantile = TRUE)) #[1] 4123 17014
dim(exprData <- gene_filter(exprData, nBins = 30, heterogeneity_threshold = 0.05)) #[1] 4123 330
save(exprData, file = "expression.RData")
} else {
load("expression.RData")
}
Clustering and dimensionality reduction
Note that due to changes to the pseudo-random number generator in R 3.6, the behavior of set.seed() has changed. Hence, the t-SNE and UMAP plots below are not exact copies of the ones in the paper, obtained with an older version of R. However, the main features of the datasets are preserved.
if(!file.exists("expression_umap_2d.RData")) {
nrow_exprData <- nrow(exprData)
ncol_exprData <- ncol(exprData)
ddist <- dist(exprData)
save(ddist, nrow_exprData, ncol_exprData, file = "expression_dist.RData")
hhc <- hclust(ddist, method = "ward.D2")
save(hhc, nrow_exprData, ncol_exprData, file = "expression_dist_hclust.RData")
pprcomp <- prcomp(exprData)
pprcomp$x <- pprcomp$x[, 1:10]
pprcomp$rotation <- pprcomp$rotation[, 1:10]
save(pprcomp, file = "expression_prcomp.RData")
set.seed(2019) # needed to get the figure in the paper
ans_Rtsne <- Rtsne(exprData, pca = FALSE, perplexity = 250) # Run TSNE
save(ans_Rtsne, file = "expression_Rtsne_2d.RData")
set.seed(2019) # needed to get the figure in the paper
ans_umap <- umap(exprData)
save(ans_umap, file = "expression_umap_2d.RData")
}
load("annotation.RData")
load("expression_dist_hclust.RData")
annotation.df <- annotation.df[hhc$labels,]
###############
save_plots <- FALSE
png_width_large <- 1500
png_height_large <- 750
png_width_small <- width_png <- 700
png_height_small <- 700
png_res <- 1/300
###################
color.bar2 <- function(x_pos, y_pos, lut, min, max=-min, nticks=11, ticks=seq(min, max, len=nticks), title='', values = NULL) {
scale = (max-min)/length(lut)*0.3
for (i in 1:length(lut)) {
y_low <- (i-1)*scale + min + y_pos
y_high <- y_low + scale
rect(x_pos,y_low,x_pos+.05,y_high, col=lut[i], border=NA)
text(x_pos+.05, (y_low + y_high)/2, values[i], adj = -0.1)
}
}
vvalues <- c("-3.0", "-2.0", "-1.3", "-0.6", " 0.0", " 0.6", " 1.3", " 2.0", " 3.0")
ffill2 <- names(table(annotation.df$CD3.col))
Main Clustering
clustering.prefix <- "CBMC"; short.prefix <- "CBMC"
clustering.df <- data.frame(cell = annotation.df$cell)
rownames(clustering.df) <- clustering.df$cell
############
mmain <- paste0("CBMC study (", nrow_exprData, " cells/", ncol_exprData, " genes)")
if(save_plots) {
mmain <- ""; png("main_dendrogram.png", width= png_width_large, height= png_height_large, res = png_res)
}
hhc$height <- hhc$height/max(hhc$height)
plot(hhc, hang = -1, labels = FALSE, xlab = "", sub = "", main = mmain)
###
wwhere <- 10
hh <- mean(c(hhc$height[length(hhc$height)-wwhere+2], hhc$height[length(hhc$height)-wwhere+1]))
tmp <- tmp_ <- as.factor(cutree(hhc, k = wwhere))
levels(tmp) <- c( "1", "1", "3", "4", "5", "6", "6", "8","4", "5")
wwhere <- length(unique(levels(tmp)))
clusteringWhere <- paste0(clustering.prefix, wwhere)
clusteringWhere.col <- paste0(clusteringWhere, ".col")
assign(clusteringWhere.col, tmp)
if(wwhere < 10) levels(tmp) <- paste0(short.prefix, wwhere, 1:wwhere) else levels(tmp) <- paste0(short.prefix, wwhere, c(paste(1:9), letters[1:(wwhere-9)]))
assign(clusteringWhere, tmp)
levels(tmp) <- c("black", "red", "green3", "blue", "cyan", "magenta")
assign(clusteringWhere.col, tmp)
tt <- table(get(clusteringWhere), get(clusteringWhere.col))
colorCode <- colnames(tt)[apply(tt, 1, which.max)]
names(colorCode) <- rownames(tt)
assign(paste0(clusteringWhere, ".colorCode"), colorCode)
clust.colorCode <- colorCode
clustering.df$tmp <- get(clusteringWhere)
clustering.df$tmp.explanatory <- clustering.df$tmp
clustering.df$tmp.col <- get(clusteringWhere.col)
ncol_ <- ncol(clustering.df)
colnames(clustering.df)[(ncol_-2):ncol_] <- c(clusteringWhere, paste0(clusteringWhere, ".explanatory"), clusteringWhere.col)
levels(clustering.df[, paste0(clusteringWhere, ".explanatory")]) <- c("CBMC1 - T Cell", "CBMC2 - B Cell", "CBMC3 - unclear", "CBMC4 - Monocyte", "CBMC5 - myeloid DC", "CBMC6 - plasmacytoid DC")
annotation.col <- annotation.df[, grep(".col", colnames(annotation.df))]
colnames(annotation.col) <- gsub(".col", "", colnames(annotation.col))
annotation.col <- annotation.col[, rev(c( "CD19", "CD3", "CD11c", "CD14","CD4","CD8", "CD2","CD57"))]
annotation.col$CBMC <- clustering.df[, ncol(clustering.df)]
colored_bars(colors = annotation.col, dend = as.dendrogram(hhc), y_scale = 0.17, y_shift = 0.015)
save(clustering.df, clust.colorCode, file = "clustering.RData")
tt <- table(clustering.df[, ncol(clustering.df)-1])
pct <- paste0(round(100*tt/sum(tt), 1), "%")
llegend <- paste(names(tt), " (", tt, "; ", pct, ")", sep = "")
tt <- table(clustering.df[, ncol(clustering.df)-1], clustering.df[, ncol(clustering.df)])
ffill <- colnames(tt)[apply(tt, 1, which.max)]
legend(700, .99, legend=llegend, fill = ffill, y.intersp = 1, box.col = "white", border = "white", title = "CBMC", title.adj = 0)
color.bar3 <- function(x_pos, y_pos, lut, min, max=-min, nticks=11, ticks=seq(min, max, len=nticks), title='', values = NULL) {
scale = (max-min)/length(lut)*0.3
for (i in 1:length(lut)) {
y_low <- (i-1)*scale + y_pos
y_high <- y_low + scale
rect(x_pos,y_low,x_pos+90,y_high, col=lut[i], border=NA)
text(x_pos+.05, (y_low + y_high)/2, values[i], adj = -2)
}
}
color.bar3(4000, 0.65, ffill2, -0.6, values = vvalues)
text(4045, 0.63, "Marker's level")
text(1, 1, "(a)")
PCA (AWST) | ADT
x_legend <- -0.2; y_legend <- 2.63
x_text <- -2.3; y_text <- 2.
load("expression_prcomp.RData")
pprcomp$x <- scale(pprcomp$x)
mmain = paste0("Principal components analysis (", nrow_exprData, " cells/", ncol_exprData, " features)")
if(save_plots) png("prcomp_CITE6.png", width= png_width_small, height= png_height_small, res = png_res)
plot(pprcomp$x, col = clustering.df$CBMC6.col, main = mmain,
xlab = "first principal component", ylab = "secondo principal component", pch = 19)
legend(x_legend, y_legend, legend=llegend, fill = ffill, y.intersp = 1, box.col = "white", border = "white", title = "CBMC", title.adj = 0)
text(x_text, y_text, "(b)")
###############################
mmain <- "principal component analysis - CD3"# "prcomp (AWST)| flow cytometry/CD3"
cat(sprintf("\n\n### %s\n\n", mmain))
principal component analysis - CD3
if(save_plots) png(file = "prcomp_CD3.png", width = width_png, height = width_png)
plot(pprcomp$x, col = as.character(annotation.df$CD3.col), main = mmain,
xlab = "first principal component", ylab = "secondo principal component", pch = 19)
ffill2 <- names(table(annotation.df$CD3.col))
color.bar2(-2.3, -0.7, ffill2, -3, values = vvalues)
text(x_text, y_text, "(a)")
mmain <- "principal component analysis - CD19"# "prcomp (AWST)| flow cytometry/CD19"
cat(sprintf("\n\n### %s\n\n", mmain))
principal component analysis - CD19
if(save_plots) png(file = "prcomp_CD19.png", width = width_png, height = width_png)
plot(pprcomp$x, col = as.character(annotation.df$CD19.col), main = mmain,
xlab = "first principal component", ylab = "secondo principal component", pch = 19)
color.bar2(-2.3, -0.7, ffill2, -3, values = vvalues)
text(x_text, y_text, "(b)")
mmain <- "principal component analysis - CD11c"# "prcomp (AWST)| flow cytometry/CD11c"
cat(sprintf("\n\n### %s\n\n", mmain))
principal component analysis - CD11c
if(save_plots) png(file = "prcomp_CD11c.png", width = width_png, height = width_png)
plot(pprcomp$x, col = as.character(annotation.df$CD11c.col), main = mmain,
xlab = "first principal component", ylab = "secondo principal component", pch = 19)
color.bar2(-2.3, -0.7, ffill2, -3, values = vvalues)
text(x_text, y_text, "(c)")
mmain <- "principal component analysis - CD14"# "prcomp (AWST)| flow cytometry/CD14"
cat(sprintf("\n\n### %s\n\n", mmain))
principal component analysis - CD14
if(save_plots) png(file = "prcomp_CD14.png", width = width_png, height = width_png)
plot(pprcomp$x, col = as.character(annotation.df$CD14.col), main = mmain,
xlab = "first principal component", ylab = "secondo principal component", pch = 19)
color.bar2(-2.3, -0.7, ffill2, -3, values = vvalues)
text(x_text, y_text, "(d)")
mmain <- "principal component analysis - CD4"# "prcomp (AWST)| flow cytometry/CD4"
cat(sprintf("\n\n### %s\n\n", mmain))
principal component analysis - CD4
if(save_plots) png(file = "prcomp_CD4.png", width = width_png, height = width_png)
plot(pprcomp$x, col = as.character(annotation.df$CD4.col), main = mmain,
xlab = "first principal component", ylab = "secondo principal component", pch = 19)
color.bar2(-2.3, -0.7, ffill2, -3, values = vvalues)
text(x_text, y_text, "(e)")
mmain <- "principal component analysis - CD8"# "prcomp (AWST)| flow cytometry/CD8"
cat(sprintf("\n\n### %s\n\n", mmain))
principal component analysis - CD8
if(save_plots) png(file = "prcomp_CD8.png", width = width_png, height = width_png)
plot(pprcomp$x, col = as.character(annotation.df$CD8.col), main = mmain,
xlab = "first principal component", ylab = "secondo principal component", pch = 19)
color.bar2(-2.3, -0.7, ffill2, -3, values = vvalues)
text(x_text, y_text, "(f)")
Rtsne p100 (AWST) | ADT
load("expression_Rtsne_2d.RData")
ans_Rtsne$Y <- scale(ans_Rtsne$Y)
if(save_plots) png("Rtsne.png", width= png_width_small, height= png_height_small, res = png_res)
mmain = "T-distributed Stochastic Neighbor Embedding (tsne)"
plot(-ans_Rtsne$Y[, 1], ans_Rtsne$Y[, 2], col = clustering.df$CBMC6.col, main = mmain, xlab = "", ylab = "")
legend(-1.65, -1.5, legend=llegend, fill = ffill, y.intersp = 1, box.col = "white", border = "white", title = "CBMC", title.adj = 0)
text(-1.65, 2, "(c)")
umap (AWST) | ADT
load("expression_umap_2d.RData")
ans_umap$layout <- scale(ans_umap$layout)
if(save_plots) png("umap.png", width= png_width_small, height= png_height_small, res = png_res)
mmain = "Uniform Manifold Approximation and Projection (umap)"
plot(-ans_umap$layout[, 2], -ans_umap$layout[, 1], col = clustering.df$CBMC6.col, main = mmain, xlab = "", ylab = "")
legend(-1.65, -1.5, legend=llegend, fill = ffill, y.intersp = 1, box.col = "white", border = "white", title = "CBMC", title.adj = 0)
text(-1.65, 1.5, "(d)")
Session info
sessionInfo()
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] dendextend_1.12.0 umap_0.2.3.1
## [3] Rtsne_0.15 EDASeq_2.18.0
## [5] ShortRead_1.42.0 GenomicAlignments_1.20.1
## [7] SummarizedExperiment_1.14.1 DelayedArray_0.10.0
## [9] matrixStats_0.55.0 Rsamtools_2.0.2
## [11] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0
## [13] Biostrings_2.52.0 XVector_0.24.0
## [15] IRanges_2.18.3 S4Vectors_0.22.1
## [17] BiocParallel_1.18.1 Biobase_2.44.0
## [19] BiocGenerics_0.30.0 awst_0.0.3
## [21] readr_1.3.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 bit64_0.9-7 RColorBrewer_1.1-2
## [4] progress_1.2.2 httr_1.4.1 tools_3.6.1
## [7] backports_1.1.5 R6_2.4.0 DBI_1.0.0
## [10] lazyeval_0.2.2 colorspace_1.4-1 gridExtra_2.3
## [13] tidyselect_0.2.5 prettyunits_1.0.2 bit_1.1-14
## [16] compiler_3.6.1 rtracklayer_1.44.4 scales_1.0.0
## [19] genefilter_1.66.0 askpass_1.1 DESeq_1.36.0
## [22] stringr_1.4.0 digest_0.6.21 rmarkdown_1.16
## [25] R.utils_2.9.0 pkgconfig_2.0.3 htmltools_0.4.0
## [28] rlang_0.4.0 RSQLite_2.1.2 hwriter_1.3.2
## [31] jsonlite_1.6 dplyr_0.8.3 R.oo_1.22.0
## [34] RCurl_1.95-4.12 magrittr_1.5 GenomeInfoDbData_1.2.1
## [37] Matrix_1.2-17 Rcpp_1.0.2 munsell_0.5.0
## [40] reticulate_1.13 viridis_0.5.1 R.methodsS3_1.7.1
## [43] stringi_1.4.3 yaml_2.2.0 zlibbioc_1.30.0
## [46] grid_3.6.1 blob_1.2.0 crayon_1.3.4
## [49] lattice_0.20-38 splines_3.6.1 GenomicFeatures_1.36.4
## [52] annotate_1.62.0 hms_0.5.1 zeallot_0.1.0
## [55] knitr_1.25 pillar_1.4.2 geneplotter_1.62.0
## [58] biomaRt_2.40.5 XML_3.98-1.20 glue_1.3.1
## [61] evaluate_0.14 latticeExtra_0.6-28 vctrs_0.2.0
## [64] gtable_0.3.0 openssl_1.4.1 purrr_0.3.2
## [67] assertthat_0.2.1 ggplot2_3.2.1 xfun_0.10
## [70] aroma.light_3.14.0 xtable_1.8-4 RSpectra_0.15-0
## [73] viridisLite_0.3.0 survival_2.44-1.1 tibble_2.1.3
## [76] AnnotationDbi_1.46.1 memoise_1.1.0